We consider the class of subgradient methods for solving minimization of a nonsmooth convex function regularized by the discretized $$\\ell _1$$1 norm models arising in image processing. This class ofdoi:10.1007/s11760-015-0815-zAbdelkrim El Mouatasim...
The cosparse analysis model as the corresponding version of the sparse synthesis model has drawn much attention in recent years. Many approaches have been proposed to solve this model. In some conventional general, these methods usually relaxedl0-norm tol1-norm orl2-norm to represent the cospas...
我们将区分两种不同等级的细节。在次梯度的“弱”微分中,即使存在更多的次梯度,目标也只是产生一个次梯度。这在实际应用中已经足够了,因为次梯度,定位(localization),切平面方法仅需要任意点一个次梯度。 另外一个更加困难的任务是将次梯度∂f(x)的完备集描述为关于x的函数,我们称这个为次梯度的“强”微分。其...
-normSVMclassifier. Introduction1 Subgradientmethods:generalizationofthegradientmethodsfor non-differentiableconvexfunctions. Motivation:Simpleandgeneralmethodforconvexproblems. Forexample,usefulforlarge-scaleLinearProgramming,Quadratic ProgrammingandSemidefiniteProgrammingproblems. Outline: Subgradientandbasiccalcu...